Elasticity of Intelligent Transportation System Applications in a Fog Computing Environment

Increasing the availability of applications is a way to improve service delivery to final consumers. This paper proposes an architecture based on fog computing and attends intelligent transportation system applications with stricter latency requirements. We also present an algorithm based on autoregressive neural networks and adaptive elasticity thresholds to guarantee applications' availability. The proposed algorithm aims to decrease the number of lost requests coming from the terminal layer. Based on workload, this algorithm analyzes the server resources in a fog-based solution and indicates the best time to increase or decrease the infrastructure. This paper also presents an experimental assessment of different scenarios to manage applications' elasticity using several techniques and policies. Finally, we compare the proposed algorithm with state-of-the-art, and its results showed a reduction of 17.8% in the number of requests lost than the others.